论文标题
有效的面部特征学习，具有广泛的基于合奏的卷积神经网络
Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural Networks
论文作者
论文摘要
传统上使用独立训练的De相关模型构建的合奏方法已被证明是减少剩余剩余概括误差的有效方法，从而为现实世界应用提供了强大而准确的方法。然而，在深度学习的背景下，培训深层网络的合奏是昂贵的，并且会产生高冗余，这效率低下。在本文中，我们介绍了基于卷积网络的共享表示形式（ESR）的合奏实验，以证明，定量和定性，将其数据处理效率和可扩展性与面部表达式的大规模数据集进行。我们表明，可以通过改变ESR的分支水平而不会损失多样性和泛化能力，从而大大降低冗余和计算负载，这对于集成性能都很重要。大规模数据集的实验表明，ESR减少了对影响网和FER+数据集的剩余残留概括误差，达到人类水平的性能，并使用情感和影响概念影响野外面部表达识别的最先进方法。
Ensemble methods, traditionally built with independently trained de-correlated models, have proven to be efficient methods for reducing the remaining residual generalization error, which results in robust and accurate methods for real-world applications. In the context of deep learning, however, training an ensemble of deep networks is costly and generates high redundancy which is inefficient. In this paper, we present experiments on Ensembles with Shared Representations (ESRs) based on convolutional networks to demonstrate, quantitatively and qualitatively, their data processing efficiency and scalability to large-scale datasets of facial expressions. We show that redundancy and computational load can be dramatically reduced by varying the branching level of the ESR without loss of diversity and generalization power, which are both important for ensemble performance. Experiments on large-scale datasets suggest that ESRs reduce the remaining residual generalization error on the AffectNet and FER+ datasets, reach human-level performance, and outperform state-of-the-art methods on facial expression recognition in the wild using emotion and affect concepts.
